Part 1: RNA

Load RNA samples

Out of 30 samples, we selected 18 for this study. These are the normal tissue samples form the control, the UVA and the UVA+SFN treatment groups. normal tissue samples from the UVB_UA groups as well as tumor samples were excluded from this analysis.
First, we removed 7,148 genes with zero counts in > 80% (> 14 out of 18) of samples. 17,273 out of 24,421 genes left.

[1] 7148
[1] 17273

Transcripts per kilobase million (TPM) normalization

Next, we noramized the counts. To convert number of hits to the relative abundane of genes in each sample, we used transcripts per kilobase million (TPM) normalization, which is as following for the j-th sample:
1. normilize for gene length: a[i, j] = 1,000*count[i, j]/gene[i, j] length(bp)
2. normalize for seq depth (i.e. total count): a(i, j)/sum(a[, j])
3. multiply by one million
A very good comparison of normalization techniques can be found at the following video:
RPKM, FPKM and TPM, clearly explained

After the normalization, each sample’s total is 1M:

02w_CON_0 02w_CON_1 02w_SFN_0 02w_SFN_1 02w_UVB_0 02w_UVB_1 15w_CON_0 15w_CON_1 15w_SFN_0 15w_SFN_1 15w_UVB_0 
    1e+06     1e+06     1e+06     1e+06     1e+06     1e+06     1e+06     1e+06     1e+06     1e+06     1e+06 
15w_UVB_1 25w_CON_0 25w_CON_1 25w_SFN_0 25w_SFN_1 25w_UVB_0 25w_UVB_1 
    1e+06     1e+06     1e+06     1e+06     1e+06     1e+06     1e+06 

Top 100 most abundant RNA molecules

# Separate top 100 abundant genes
tmp <- droplevels(tpm[Geneid %in% levels(tpm$Geneid)[(nrow(tpm) - 99):nrow(tpm)]])

tmp <- melt.data.table(data = tmp,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(tmp),
                       variable.name = "Sample",
                       value.name = "TPM")

tmp$Week <- substr(x = tmp$Sample,
                   start = 1,
                   stop = 3)
tmp$Week <- factor(tmp$Week,
                   levels = unique(tmp$Week))


tmp$Treatment <- substr(x = tmp$Sample,
                        start = 5,
                        stop = 7)
tmp$Treatment <- factor(tmp$Treatment,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"))

tmp$Replica <- substr(x = tmp$Sample,
                      start = 9,
                      stop = 9)
tmp$Replica <- factor(tmp$Replica,
                      levels = 0:1)

# Plot top 100 abundant genes
p2 <- ggplot(tmp,
             aes(x = TPM,
                 y = Geneid,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed")
ggplotly(p2)

Bottom 100 least abundant RNA molecules

tmp <- droplevels(tpm[Geneid %in% levels(tpm$Geneid)[1:100]])

tmp <- melt.data.table(data = tmp,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(tmp),
                       variable.name = "Sample",
                       value.name = "TPM")

tmp$Week <- substr(x = tmp$Sample,
                   start = 1,
                   stop = 3)
tmp$Week <- factor(tmp$Week,
                   levels = unique(tmp$Week))


tmp$Treatment <- substr(x = tmp$Sample,
                        start = 5,
                        stop = 7)
tmp$Treatment <- factor(tmp$Treatment,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"))

tmp$Replica <- substr(x = tmp$Sample,
                      start = 9,
                      stop = 9)
tmp$Replica <- factor(tmp$Replica,
                      levels = 0:1)

# Plot top 100 abundant genes
p3 <- ggplot(tmp,
             aes(x = TPM,
                 y = Geneid,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed")
ggplotly(p3)

Meta data

dmeta <- data.table(Sample = colnames(dt1)[-c(1:2)])

dmeta$time <- substr(x = dmeta$Sample,
                   start = 1,
                   stop = 3)
dmeta$time <- factor(dmeta$time,
                   levels = c("02w",
                              "15w",
                              "25w"))
dmeta$Week <- factor(dmeta$time,
                   levels = c("02w",
                              "15w",
                              "25w"),
                   labels = c("Week 2",
                              "Week 15",
                              "Week 25"))

dmeta$trt <- substr(x = dmeta$Sample,
                        start = 5,
                        stop = 7)
dmeta$trt <- factor(dmeta$trt,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"))
dmeta$Treatment <- factor(dmeta$trt,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"),
                        labels = c("Negative Control",
                                   "Positive Control (UVB)",
                                   "Sulforaphane (SFN)"))

dmeta$Replica <- substr(x = dmeta$Sample,
                      start = 9,
                      stop = 9)
dmeta$Replica <- factor(dmeta$Replica,
                      levels = 0:1)

datatable(dmeta,
          options = list(pageLength = nrow(dmeta)))

PCA of TPM

NOTE: the distributions are skewed. To make them symmetric, log transformation is often applied. However, there is an issue of zeros. In this instance, we added a small values lambda[i] equal to 1/10 of the smallest non-zero value of i-th gene.

dm.tpm <- as.matrix(tpm[, -c(1:2), with = FALSE])
rownames(dm.tpm) <- tpm$Geneid

# Add lambdas to all values, then take a log
dm.ltpm <- t(apply(X = dm.tpm,
                      MARGIN = 1,
                      FUN = function(a) {
                        lambda <- min(a[a > 0])/10
                        log(a + lambda)
                      }))

# PCA----
m1 <- prcomp(t(dm.ltpm),
             center = TRUE,
             scale. = TRUE)

summary(m1)
Importance of components:
                           PC1     PC2     PC3      PC4      PC5      PC6      PC7      PC8      PC9     PC10
Standard deviation     70.7928 56.9107 50.8898 28.84564 26.51968 24.81005 23.85276 22.63644 20.97344 20.20442
Proportion of Variance  0.2901  0.1875  0.1499  0.04817  0.04072  0.03564  0.03294  0.02967  0.02547  0.02363
Cumulative Proportion   0.2901  0.4777  0.6276  0.67575  0.71647  0.75211  0.78504  0.81471  0.84018  0.86381
                           PC11     PC12     PC13     PC14     PC15     PC16     PC17      PC18
Standard deviation     19.24099 19.01279 18.73783 18.53642 17.87923 17.65132 17.16891 2.134e-13
Proportion of Variance  0.02143  0.02093  0.02033  0.01989  0.01851  0.01804  0.01707 0.000e+00
Cumulative Proportion   0.88524  0.90617  0.92650  0.94639  0.96490  0.98293  1.00000 1.000e+00
plot(m1)


# Biplot while keep only the most important variables (Javier)----
# Select PC-s to pliot (PC1 & PC2)
choices <- c(1:3)

# Scores, i.e. points (df.u)
dt.scr <- data.table(m1$x[, choices])
# Add grouping variables
dt.scr$trt <- dmeta$trt
dt.scr$time <- dmeta$time
dt.scr$sample <- dmeta$Sample

# Loadings, i.e. arrows (df.v)
dt.rot <- as.data.frame(m1$rotation[, choices])
dt.rot$feat <- rownames(dt.rot)
dt.rot <- data.table(dt.rot)

# Axis labels
u.axis.labs <- paste(colnames(dt.rot)[choices], 
                     sprintf('(%0.1f%% explained var.)', 
                             100*m1$sdev[choices]^2/sum(m1$sdev^2)))

p1 <- ggplot(data = dt.scr,
             aes(x = PC1,
                 y = PC2,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2])
ggplotly(p1)


p1 <- ggplot(data = dt.scr,
             aes(x = PC1,
                 y = PC3,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[3])
ggplotly(p1)


p1 <- ggplot(data = dt.scr,
             aes(x = PC2,
                 y = PC3,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[2]) +
  scale_y_continuous(u.axis.labs[3])
ggplotly(p1)
scatterplot3js(x = dt.scr$PC1, 
               y = dt.scr$PC2, 
               z = dt.scr$PC3, 
               color = as.numeric(dt.scr$trt),
               renderer = "auto",
               pch = dt.scr$sample,
               size = 0.1)

Differential expressions

dtm <- as.matrix(dt1[, -c(1:2), with = FALSE])
rownames(dtm) <- dt1$Geneid

dds <- DESeqDataSetFromMatrix(countData = dtm, 
                              colData = dmeta,
                              ~ trt + time)

# If all samples contain zeros, geometric means cannot be
# estimated. Change default 'type = "ratio"' to 'type = "poscounts"'.
# Type '?DESeq2::estimateSizeFactors' for more details.
dds <- estimateSizeFactors(dds,
                           type = "poscounts")
dds
class: DESeqDataSet 
dim: 17273 18 
metadata(1): version
assays(1): counts
rownames(17273): Xkr4 Mrpl15 ... Zf12 Erdr1
rowData names(0):
colnames(18): 02w_CON_0 02w_CON_1 ... 25w_UVB_0 25w_UVB_1
colData names(7): Sample time ... Replica sizeFactor
# # Set cores for parallel processing of DESeq----
# snowparam <- SnowParam(workers = snowWorkers(), 
#                        type = "SOCK")
# register(snowparam, 
#          default = TRUE)

# Run DESeq----
dds <- DESeq(dds,
             fitType = "local",
             parallel = TRUE)
using pre-existing size factors
estimating dispersions
gene-wise dispersion estimates: 2 workers
'package:stats' may not be available when loading'package:stats' may not be available when loadingmean-dispersion relationship
final dispersion estimates, fitting model and testing: 2 workers
'package:stats' may not be available when loading'package:stats' may not be available when loading
results(dds)
log2 fold change (MLE): time 25w vs 02w 
Wald test p-value: time 25w vs 02w 
DataFrame with 17273 rows and 6 columns
                baseMean     log2FoldChange              lfcSE               stat               pvalue
               <numeric>          <numeric>          <numeric>          <numeric>            <numeric>
Xkr4   0.372637436040936  0.347674453012715   1.85290358759446  0.187637638213051    0.851160719227371
Mrpl15  473.968965072559  -0.12450153923719 0.0714282207122831  -1.74303010764736   0.0813283558381948
Lypla1  1265.59579157719 -0.523495263510286 0.0738820243170689  -7.08555657955006 1.38486574082199e-12
Tcea1   347.786701718151 -0.266798692902443 0.0877732341254237  -3.03963612097511  0.00236864137641766
Rgs20   393.200729771858 -0.465618636782497  0.112301880629506  -4.14613392200096 3.38136101125416e-05
...                  ...                ...                ...                ...                  ...
Vamp7   463.037463939562 -0.397578416634149  0.106731090646671  -3.72504782088582 0.000195278199520461
Spry3   4.03688536326271  -1.37585526169675  0.714812221938873   -1.9247785914528   0.0542570634088351
Tmlhe    41.145292290645 -0.469773091455135  0.222960586656882  -2.10697818165538   0.0351194708601783
Zf12   0.216082295697271 0.0868343182457816   2.02714964711862 0.0428356724276411    0.965832527590861
Erdr1   19.5763157162845  0.268086319281388   0.59510270258956   0.45048748411799    0.652358976804483
                       padj
                  <numeric>
Xkr4                     NA
Mrpl15    0.173491055505417
Lypla1 3.51250323704544e-10
Tcea1    0.0109771852968187
Rgs20   0.00035467560890265
...                     ...
Vamp7   0.00144782110421568
Spry3     0.127293298490708
Tmlhe    0.0916707944323755
Zf12                     NA
Erdr1     0.767483982679305
resultsNames(dds)
[1] "Intercept"       "trt_UVB_vs_CON"  "trt_SFN_vs_CON"  "time_15w_vs_02w" "time_25w_vs_02w"
colData(dds)
DataFrame with 18 rows and 7 columns
               Sample     time     Week      trt              Treatment  Replica        sizeFactor
          <character> <factor> <factor> <factor>               <factor> <factor>         <numeric>
02w_CON_0   02w_CON_0      02w   Week 2      CON       Negative Control        0 0.948964359312974
02w_CON_1   02w_CON_1      02w   Week 2      CON       Negative Control        1 0.434767245904826
02w_SFN_0   02w_SFN_0      02w   Week 2      SFN     Sulforaphane (SFN)        0 0.837004805606802
02w_SFN_1   02w_SFN_1      02w   Week 2      SFN     Sulforaphane (SFN)        1 0.937831868319037
02w_UVB_0   02w_UVB_0      02w   Week 2      UVB Positive Control (UVB)        0 0.949001244715886
...               ...      ...      ...      ...                    ...      ...               ...
25w_CON_1   25w_CON_1      25w  Week 25      CON       Negative Control        1  1.14536599296614
25w_SFN_0   25w_SFN_0      25w  Week 25      SFN     Sulforaphane (SFN)        0  1.03002696492621
25w_SFN_1   25w_SFN_1      25w  Week 25      SFN     Sulforaphane (SFN)        1  1.07022971587466
25w_UVB_0   25w_UVB_0      25w  Week 25      UVB Positive Control (UVB)        0  1.21285056237317
25w_UVB_1   25w_UVB_1      25w  Week 25      UVB Positive Control (UVB)        1  1.01927326280773

Session Information

sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] threejs_0.3.1               igraph_1.2.4.1              plotly_4.9.0                ggplot2_3.1.1              
 [5] readxl_1.3.1                DESeq2_1.24.0               SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
 [9] BiocParallel_1.17.18        matrixStats_0.54.0          Biobase_2.44.0              GenomicRanges_1.36.0       
[13] GenomeInfoDb_1.20.0         IRanges_2.18.1              S4Vectors_0.22.0            BiocGenerics_0.30.0        
[17] DT_0.6                      data.table_1.12.2           knitr_1.22                 

loaded via a namespace (and not attached):
 [1] httr_1.4.0             tidyr_0.8.3            viridisLite_0.3.0      jsonlite_1.6          
 [5] bit64_0.9-7            splines_3.6.0          shiny_1.3.2            Formula_1.2-3         
 [9] assertthat_0.2.1       latticeExtra_0.6-28    blob_1.1.1             GenomeInfoDbData_1.2.1
[13] cellranger_1.1.0       yaml_2.2.0             pillar_1.4.1           RSQLite_2.1.1         
[17] backports_1.1.4        lattice_0.20-38        glue_1.3.1             digest_0.6.19         
[21] promises_1.0.1         RColorBrewer_1.1-2     XVector_0.24.0         checkmate_1.9.3       
[25] colorspace_1.4-1       httpuv_1.5.1           htmltools_0.3.6        Matrix_1.2-17         
[29] plyr_1.8.4             XML_3.98-1.19          pkgconfig_2.0.2        genefilter_1.66.0     
[33] zlibbioc_1.30.0        purrr_0.3.2            xtable_1.8-4           snow_0.4-3            
[37] scales_1.0.0           later_0.8.0            htmlTable_1.13.1       tibble_2.1.2          
[41] annotate_1.62.0        withr_2.1.2            nnet_7.3-12            lazyeval_0.2.2        
[45] mime_0.6               survival_2.44-1.1      magrittr_1.5           crayon_1.3.4          
[49] memoise_1.1.0          foreign_0.8-71         tools_3.6.0            stringr_1.4.0         
[53] munsell_0.5.0          locfit_1.5-9.1         cluster_2.0.9          AnnotationDbi_1.46.0  
[57] compiler_3.6.0         rlang_0.3.4            grid_3.6.0             RCurl_1.95-4.12       
[61] rstudioapi_0.10        htmlwidgets_1.3        crosstalk_1.0.0        labeling_0.3          
[65] bitops_1.0-6           base64enc_0.1-3        gtable_0.3.0           DBI_1.0.0             
[69] R6_2.4.0               gridExtra_2.3          dplyr_0.8.1            bit_1.1-14            
[73] Hmisc_4.2-0            stringi_1.4.3          Rcpp_1.0.1             geneplotter_1.62.0    
[77] rpart_4.1-15           acepack_1.4.1          tidyselect_0.2.5       xfun_0.7              
---
title: "Skin UVB SKH1 mouse model treated with SFN "
output:
  html_notebook:
    toc: yes
    toc_float: yes
    code_folding: hide
---

# Part 1: RNA
```{r header, echo = FALSE, message = FALSE, error = FALSE, warning  =FALSE}
# if (!requireNamespace("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install("DESeq2")

require(knitr)
require(data.table)
require(DT)
require(DESeq2)
require(readxl)
require(BiocParallel)
require(ggplot2)
require(plotly)
require(threejs)

# NOTE: on DESeq2 Output: 'baseMean' is the average of the normalized count values, 
# divided by the size factors, taken over all samples in the DESeqDataSet
```

## Load RNA samples
Out of 30 samples, we selected 18 for this study. These are the normal tissue samples form the control, the UVA and the UVA+SFN treatment groups. normal tissue samples from the UVB_UA groups as well as tumor samples were excluded from this analysis.     
First, we removed 7,148 genes with zero counts in > 80% (> 14 out of 18) of samples. 17,273 out of 24,421 genes left. 
         
```{r data_rna, warning = FALSE, echo = FALSE, message = FALSE}
# Load data----
dt0 <- fread("data/renyi_dedup_rnaseq_data/featurescounts_uvb-skin_dedup_renyi_2-9-2018.csv",
             skip = 1)

# Remove unused columns----
dt1 <- dt0[, c(1, 6:ncol(dt0)), with = FALSE]

cnames <- colnames(dt1)[-c(1:2)]
cnames <- gsub(x = cnames,
               pattern = ".dedup.bam",
               replacement = "")
colnames(dt1)[-c(1:2)] <- cnames

# ATTENTION! In this analysis, we will only examine controls and SFN
# Also, removed cancer cell samples
tnames <- substr(x = colnames(dt1), 
                 start = 3,
                 stop = 3)

gnames <- substr(x = colnames(dt1), 
                 start = 5,
                 stop = 7)

dt1 <- dt1[, gnames %in% c("id",
                           "th",
                           "CON",
                           "UVB",
                           "SFN" ) &
             tnames != "t",
           with = FALSE]
# 18 samples left

# Remove genes with zero counts in > 80% (> 14 out of 18) of samples
tmp <- dt1[, -c(1:2)] == 0
tmp <- rowSums(tmp) > 14
sum(tmp)

dt1 <- droplevels(dt1[!tmp, ])
nrow(dt1)
# 17,273 out of 24,421 genes left

datatable(head(dt1, 10),
              rownames = FALSE,
              options = list(pageLength = 10),
              caption = "Table 1: first 10 rows of the count table")
```

## Transcripts per kilobase million (TPM) normalization
Next, we noramized the counts. To convert number of hits to  the relative abundane of genes in each sample, we used ***transcripts per kilobase million (TPM)*** normalization, which is as following for the j-th sample:       
1. normilize for gene length: a[i, j] = 1,000*count[i, j]/gene[i, j] length(bp)     
2. normalize for seq depth (i.e. total count): a(i, j)/sum(a[, j])     
3. multiply by one million     
A very good comparison of normalization techniques can be found at the following video:    
[RPKM, FPKM and TPM, clearly explained](https://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained/)
     
After the normalization, each sample's total is 1M:
     
```{r tpm, warning = FALSE, echo = FALSE, message = FALSE}
# Normalize counts to TPM
tmp <- 1000*dt1[, 3:ncol(dt1)]/dt1$Length
tpm <- data.table(Geneid = dt1$Geneid,
                  Length = dt1$Length,
                  apply(tmp,
                        2,
                        function(a) {
                          10^6*(a/sum(a))
                        }))
colSums(tpm[, -c(1:2)])

formatRound(datatable(head(tpm, 10),
                      rownames = FALSE,
                      options = list(pageLength = 10),
                      caption = "Table 2: transcripts per kilobase million (TPM) normalized counts"),
            columns = 3:ncol(tpm),
            digits = 2)

# Total TPM
total <- rowSums(tpm[, 3:ncol(tpm)])

# Sort genes by relative abundancy
tpm$Geneid <- factor(tpm$Geneid ,
                     levels = tpm$Geneid[order(total,
                                               decreasing = FALSE)])
```

# Top 100 most abundant RNA molecules
```{r most_abundant}
# Separate top 100 abundant genes
tmp <- droplevels(tpm[Geneid %in% levels(tpm$Geneid)[(nrow(tpm) - 99):nrow(tpm)]])

tmp <- melt.data.table(data = tmp,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(tmp),
                       variable.name = "Sample",
                       value.name = "TPM")

tmp$Week <- substr(x = tmp$Sample,
                   start = 1,
                   stop = 3)
tmp$Week <- factor(tmp$Week,
                   levels = unique(tmp$Week))


tmp$Treatment <- substr(x = tmp$Sample,
                        start = 5,
                        stop = 7)
tmp$Treatment <- factor(tmp$Treatment,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"))

tmp$Replica <- substr(x = tmp$Sample,
                      start = 9,
                      stop = 9)
tmp$Replica <- factor(tmp$Replica,
                      levels = 0:1)

# Plot top 100 abundant genes
p2 <- ggplot(tmp,
             aes(x = TPM,
                 y = Geneid,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed")
ggplotly(p2)
```

# Bottom 100 least abundant RNA molecules
```{r least_abundant}
tmp <- droplevels(tpm[Geneid %in% levels(tpm$Geneid)[1:100]])

tmp <- melt.data.table(data = tmp,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(tmp),
                       variable.name = "Sample",
                       value.name = "TPM")

tmp$Week <- substr(x = tmp$Sample,
                   start = 1,
                   stop = 3)
tmp$Week <- factor(tmp$Week,
                   levels = unique(tmp$Week))


tmp$Treatment <- substr(x = tmp$Sample,
                        start = 5,
                        stop = 7)
tmp$Treatment <- factor(tmp$Treatment,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"))

tmp$Replica <- substr(x = tmp$Sample,
                      start = 9,
                      stop = 9)
tmp$Replica <- factor(tmp$Replica,
                      levels = 0:1)

# Plot top 100 abundant genes
p3 <- ggplot(tmp,
             aes(x = TPM,
                 y = Geneid,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed")
ggplotly(p3)
```

# Meta data
```{r meta}
dmeta <- data.table(Sample = colnames(dt1)[-c(1:2)])

dmeta$time <- substr(x = dmeta$Sample,
                   start = 1,
                   stop = 3)
dmeta$time <- factor(dmeta$time,
                   levels = c("02w",
                              "15w",
                              "25w"))
dmeta$Week <- factor(dmeta$time,
                   levels = c("02w",
                              "15w",
                              "25w"),
                   labels = c("Week 2",
                              "Week 15",
                              "Week 25"))

dmeta$trt <- substr(x = dmeta$Sample,
                        start = 5,
                        stop = 7)
dmeta$trt <- factor(dmeta$trt,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"))
dmeta$Treatment <- factor(dmeta$trt,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"),
                        labels = c("Negative Control",
                                   "Positive Control (UVB)",
                                   "Sulforaphane (SFN)"))

dmeta$Replica <- substr(x = dmeta$Sample,
                      start = 9,
                      stop = 9)
dmeta$Replica <- factor(dmeta$Replica,
                      levels = 0:1)

datatable(dmeta,
          options = list(pageLength = nrow(dmeta)))
```

# PCA of TPM
NOTE: the distributions are skewed. To make them symmetric, log transformation is often applied. However, there is an issue of zeros. In this instance, we added a small values ***lambda[i]*** equal to 1/10 of the smallest non-zero value of *i*-th gene. 
```{r pca}
dm.tpm <- as.matrix(tpm[, -c(1:2), with = FALSE])
rownames(dm.tpm) <- tpm$Geneid

# Add lambdas to all values, then take a log
dm.ltpm <- t(apply(X = dm.tpm,
                      MARGIN = 1,
                      FUN = function(a) {
                        lambda <- min(a[a > 0])/10
                        log(a + lambda)
                      }))

# PCA----
m1 <- prcomp(t(dm.ltpm),
             center = TRUE,
             scale. = TRUE)

summary(m1)
plot(m1)

# Biplot while keep only the most important variables (Javier)----
# Select PC-s to pliot (PC1 & PC2)
choices <- c(1:3)

# Scores, i.e. points (df.u)
dt.scr <- data.table(m1$x[, choices])
# Add grouping variables
dt.scr$trt <- dmeta$trt
dt.scr$time <- dmeta$time
dt.scr$sample <- dmeta$Sample

# Loadings, i.e. arrows (df.v)
dt.rot <- as.data.frame(m1$rotation[, choices])
dt.rot$feat <- rownames(dt.rot)
dt.rot <- data.table(dt.rot)

# Axis labels
u.axis.labs <- paste(colnames(dt.rot)[choices], 
                     sprintf('(%0.1f%% explained var.)', 
                             100*m1$sdev[choices]^2/sum(m1$sdev^2)))

p1 <- ggplot(data = dt.scr,
             aes(x = PC1,
                 y = PC2,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2])
ggplotly(p1)

p1 <- ggplot(data = dt.scr,
             aes(x = PC1,
                 y = PC3,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[3])
ggplotly(p1)

p1 <- ggplot(data = dt.scr,
             aes(x = PC2,
                 y = PC3,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[2]) +
  scale_y_continuous(u.axis.labs[3])
ggplotly(p1)
```

```{r pca_3d, fig.height = 10, fig.width = 10}
scatterplot3js(x = dt.scr$PC1, 
               y = dt.scr$PC2, 
               z = dt.scr$PC3, 
               color = as.numeric(dt.scr$trt),
               renderer = "auto",
               pch = dt.scr$sample,
               size = 0.1)
```


# Differential expressions
```{r deseq2}
dtm <- as.matrix(dt1[, -c(1:2), with = FALSE])
rownames(dtm) <- dt1$Geneid

dds <- DESeqDataSetFromMatrix(countData = dtm, 
                              colData = dmeta,
                              ~ trt + time)

# If all samples contain zeros, geometric means cannot be
# estimated. Change default 'type = "ratio"' to 'type = "poscounts"'.
# Type '?DESeq2::estimateSizeFactors' for more details.
dds <- estimateSizeFactors(dds,
                           type = "poscounts")
dds

# # Set cores for parallel processing of DESeq----
# snowparam <- SnowParam(workers = snowWorkers(), 
#                        type = "SOCK")
# register(snowparam, 
#          default = TRUE)

# Run DESeq----
dds <- DESeq(dds,
             fitType = "local",
             parallel = TRUE)
results(dds)
resultsNames(dds)
colData(dds)
```


# Session Information
```{r info,eval=TRUE}
sessionInfo()
```